Generalizable Sensor-Based Activity Recognition via Categorical Concept Invariant Learning

TL;DR

This paper introduces a novel Categorical Concept Invariant Learning framework for human activity recognition that enhances model generalization across diverse subjects and conditions by focusing on feature and logit invariance.

Contribution

It proposes a concept matrix regularization method that improves domain-invariant feature learning for better generalization in HAR tasks.

Findings

Outperforms state-of-the-art methods on four HAR benchmarks.

Effective in cross-person, cross-dataset, and cross-position scenarios.

Enhances generalization to unseen subjects and conditions.

Abstract

Human Activity Recognition (HAR) aims to recognize activities by training models on massive sensor data. In real-world deployment, a crucial aspect of HAR that has been largely overlooked is that the test sets may have different distributions from training sets due to inter-subject variability including age, gender, behavioral habits, etc., which leads to poor generalization performance. One promising solution is to learn domain-invariant representations to enable a model to generalize on an unseen distribution. However, most existing methods only consider the feature-invariance of the penultimate layer for domain-invariant learning, which leads to suboptimal results. In this paper, we propose a Categorical Concept Invariant Learning (CCIL) framework for generalizable activity recognition, which introduces a concept matrix to regularize the model in the training stage by simultaneously concentrating on feature-invariance and logit-invariance. Our key idea is that the concept matrix for samples belonging to the same activity category should be similar. Extensive experiments on four public HAR benchmarks demonstrate that our CCIL substantially outperforms the state-of-the-art approaches under cross-person, cross-dataset, cross-position, and one-person-to-another settings.